System and method for reconstructing cardiac activation information

ABSTRACT

An example method of representing cardiac information associated with a heart rhythm disorder is disclosed. The method includes accessing a plurality of neighboring cardiac signals obtained from a patient. The method also includes eliminating far-field activations from the plurality of neighboring cardiac signals using one or more divergence criteria that define local activations in the plurality of neighboring cardiac signals, the divergence criteria being associated with divergence among the plurality of neighboring cardiac signals. The method further includes constructing a clinical representation of local activations in the plurality of neighboring cardiac signals.

RELATED APPLICATIONS

This application is a continuation-in-part of U.S. application Ser. No.14/074,619, filed Nov. 7, 2013, which is a continuation of U.S.application Ser. No. 13/438,534, filed Apr. 3, 2012, now U.S. Pat. No.8,594,777, which is a continuation of U.S. application Ser. No.13/217,123, filed on Aug. 24, 2011, now U.S. Pat. No. 8,165,666, whichclaims the benefit of the priority of U.S. Provisional Application No.61/481,607, filed May 2, 2011, each of which is incorporated herein byreference in its entirety.

GOVERNMENT RIGHTS

This invention was made with government support under Grants R01HL83359, HL83359-S1 and HL103800 from the National Institutes of Health.The government has certain rights in the invention.

BACKGROUND

1. Field

The present application relates generally to heart rhythm disorders.More specifically, the present application is directed to a system andmethod for reconstructing cardiac activation information (activationonset) associated with heart rhythm disorders.

2. Brief Discussion of Related Art

Heart (cardiac) rhythm disorders are common and represent significantcauses of morbidity and death throughout the world. Malfunction of theelectrical system in the heart represents a proximate cause of heartrhythm disorders. Heart rhythm disorders exist in many forms, of whichthe most complex and difficult to treat are atrial fibrillation (AF),ventricular tachycardia (VT) and ventricular fibrillation (VF). Otherrhythm disorders are more simple to treat, but may also be clinicallysignificant including atrial tachycardia (AT), supraventriculartachycardia (SVT), atrial flutter (AFL), supraventricular ectopiccomplexes/beats (SVE) and premature ventricular complexes/beats (PVC).While under normal conditions the sinus node keeps the heart in sinusrhythm, under certain conditions rapid activation of the normal sinusnode can cause inappropriate sinus tachycardia or sinus node reentry,both of which also represent heart rhythm disorders.

Treatment of heart rhythm disorders —particularly complex rhythmdisorders of AF, VF and polymorphic VT—can be very difficult.Pharmacologic therapy for complex rhythm disorder is not optimal, withpoor efficacy and significant side effects. Ablation has been usedincreasingly in connection with heart rhythm disorders by maneuvering asensor/probe to the heart through the blood vessels, or directly atsurgery, and delivering energy to a location of the heart that harbors acause of the heart rhythm disorder to mitigate and in some cases toeliminate the heart rhythm disorder. However, in complex rhythmdisorders ablation is often difficult and ineffectual because tools thatidentify and locate a cause of the heart rhythm disorder are poor andhinder attempts to deliver energy to the correct region of the heart toeliminate the disorder.

Certain systems and methods are known for treating simple heart rhythmdisorders. In a simple heart rhythm disorder (e.g., atrial tachycardia),consistent activation onset patterns from beat to beat can generally betraced back to an earliest location, which can be ablated to mitigateand in some cases to eliminate the disorder. Even in simple heart rhythmdisorders, such ablation of the cause of a heart rhythm disorder ischallenging and experienced practitioners often require hours to ablatesimple rhythm disorders with consistent beat-to-beat activationpatterns, such as atrial tachycardia.

There are no known systems and methods that have been successful withrespect to identifying causes for the complex rhythm disorders such asAF, VF or polymorphic VT. In a complex rhythm disorder, an earliestlocation of activation onsets cannot be identified because activationonset patterns change from beat to beat, and are “continuous” such thatthere is no identifiable earliest point (or start) or latest point (orend).

Diagnosing and treating heart rhythm disorders often involves theintroduction of a catheter having a plurality of sensors/probes into theheart through the blood vessels of a patient. The sensors detectelectric activity of the heart at sensor locations in the heart. Theelectric activity is generally processed into electrogram signals thatrepresent the activation of the heart at the sensor locations.

In a simple heart rhythm disorder, the signal at each sensor location isgenerally consistent from beat to beat in timing and often in shape andnumber of its deflections, enabling identification of activation onsetsat each sensor location. However, in a complex rhythm disorder, thesignal at each sensor location from beat to beat may transition betweenone, several, and multiple deflections of various shapes. For instance,when a signal for a sensor location in AF includes 5, 7, 11 or moredeflections, it is difficult if not impossible to identify whichdeflections in the signal are at or near the sensor location in theheart (i.e., local activation) versus a further removed location stillsensed by the sensor in the heart (i.e., far-field activation) or simplynoise from another part of the patient's heart, other anatomicstructures, movement or motion of the sensor relative to the heart orexternal electronic systems.

There are no known systems and methods that have been able toreconstruct cardiac activation information (onsets) in variously shapedsignals associated with heart rhythm disorders, especially in complexrhythm disorders, to facilitate identification of a cause of the heartrhythm disorders and their elimination.

SUMMARY

The present invention is applicable to reconstructing activationinformation of various rhythm disorders, including heart rhythmdisorders, as well as other biological rhythm disorders, such asneurological seizures, esophageal spasms, bladder instability, irritablebowel syndrome, and other biological disorders for which biologicalactivation information can be reconstructed to permit determination,diagnosis, and/or treatment of the cause or source of the disorders. Itis particularly useful, however, in complex rhythm disorders whichresult in complex activation patterns, and especially useful in complexrhythm disorders of the heart, in order to find the cause(s) orsource(s) of the disorders such that they can be treated withexpediency.

Local activation is activation that originates from, or is associatedwith, a specific location in the heart (sensed location). A sensor maybe proximate to the sensed location (if it is a sensor in direct contactwith the sensed location) or associated with the sensed location (if itis a sensor not in direct contact with the sensed location). Far-fieldactivation, on the other hand, is activation that originates at alocation in the heart that is different than the sensed locationassociated with the sensor.

Conventionally, local activation has been clinically detected bycharacteristic patterns in individual signals. For example, in a simplefocal arrhythmia, conventionally, monophasic negative (“QS”) complexeson a unipolar electrogram characterize the origin of a simple focalarrhythmia. However, in complex rhythm disorders, colliding electricalwaves may be superimposed upon this local activation making detectionvia such conventional methods difficult or impossible. This isindependent of relative sensor locations. Thus, in a complex rhythmdisorder, even sensors associated with sensed locations that are closetogether will have local and far-field activations superimposed.Additionally, sensors associated with, but not in direct contact with,with the sensed locations, will have varying local and far-fieldactivations (e.g., due to factors such as movement, respiration, heartmotion) that further limit the ability to separate local and far-fieldactivations.

The ability of the present invention to reliably identify and representlocal activation, by eliminating far-field activations from clinicalrepresentation, enables accurate detection of rotor and focal sourcesassociated with complex rhythm disorders, and facilitates directtargeting of the sources for therapy. Conversely, approaches that do noteliminate far-field activation are less able to detect consistentsources, partly because their analysis is limited to time intervals whenfar-field activations (e.g., ventricular activation, colliding waves)are less evident or absent. This limitation of prior methods limits orprecludes the detection of sources, which is made possible by thisinvention.

Complex heart rhythm disorders typically result in activation patternsthat are extremely difficult to decipher and the ability to determineaccurate activation information of heartbeats in complex disorders haspreviously not been possible. Among the advantages of the presentinvention is the ability to reconstruct cardiac activation informationsuch that a determination of the cause and/or source of the disorder canbe determined and treated. Another advantage is that the presentinvention provides a system and method which can be carried out rapidlywhile a sensing device—such as a catheter having sensors thereon—is usedin or near the patient and can be followed by treatment of cardiactissue to ameliorate the disorder and in many cases cure the disorder.Treatment may thus occur immediately upon computing the reconstructedcardiac information, since it will provide the location(s) of the causeor source of the disorder.

Prior systems and methods suffered from the inability to determine thesource of heart rhythm disorders and consequently provided no means oftargeting the source for meaningful and curative treatment.Additionally, prior systems and methods required numerous and complexsteps of treatment and yet still failed to provide a means ofreconstructing cardiac activation information sufficient to identify thecause(s) or source(s) of the heart rhythm disorder.

In contrast to prior systems and methods, the present invention providesa relatively few number of steps to reconstruct the activationinformation in order to determine the activation onset times at varioussensor locations for a heartbeat amidst the virtually indiscernibleactivation patterns.

As used herein, reconstruction is a process of identifying activationonset time in a cardiac or biological signal at a sensor locationdistinct from nearby or adjacent sensor locations for one or more beatsof a biological or cardiac rhythm disorder.

As used herein, activation onset time is a time point at whichactivation commences in a cell or tissue, as opposed to other timepoints during activation.

As used herein, activation is a process whereby a cell commences itsoperation from a quiescent (diastolic) state to an active (electrical)state.

In accordance with an embodiment or aspect, an example method ofrepresenting cardiac information associated with a heart rhythm disorderis disclosed. The method includes accessing a plurality of neighboringcardiac signals obtained from a patient. The method also includeseliminating far-field activations from the plurality of neighboringcardiac signals using one or more divergence criteria that define localactivations in the plurality of neighboring cardiac signals, thedivergence criteria being associated with divergence among the pluralityof neighboring cardiac signals.

Elimination of the far-field activation from the plurality ofneighboring signals includes the following actions. A first cardiacsignal and a second cardiac signal of the plurality of neighboringsignals are accessed. The first cardiac signal and the second cardiacsignal are processed to determine whether there is a point of change inthe first cardiac signal at which a derivative of the first cardiacsignal diverges with respect to a derivative of the second cardiacsignal above a threshold. An activation onset time is assigned in thefirst cardiac signal at the point of change to define a local activationif the point of change is in the first cardiac signal. The point ofchange can be determined at about the same time point for the firstcardiac signal and the second cardiac signal.

The determination of the point of change can include the followingactions. A composite cardiac signal can be formed from the first cardiacsignal and the second cardiac signal. Ratio values at a plurality ofpoints in the first cardiac signal can be determined. Each ratio valuecan represent a difference between the derivative of the second cardiacsignal and a derivative of the composite cardiac signal to a differencebetween derivative of the first cardiac signal and the derivative of thecomposite cardiac signal. A point having a largest ratio value from thedetermined ratio values can be selected as the point of change in thefirst cardiac signal.

If there is no point of change in the first cardiac signal, at least onecharacteristic of the first cardiac signal can be matched to at leastone characteristic of a reference cardiac signal in a catalog of cardiacsignals. Thereafter, an activation onset time of the reference cardiacsignal can then be assigned as an activation onset time in the firstcardiac signal to define a local activation in the first cardiac signal.

The method of representing cardiac information associated with a heartrhythm disorder can further include iteratively accessing pairs ofcardiac signals from the plurality of neighboring cardiac signals. Eachpair includes a first cardiac signal and different second cardiacsignal. The processing and assigning for each of the pairs can beperformed to define multiple local activations in the first cardiacsignal in each of the pairs. Thereafter, the clinical represented can beconstructed based on multiple local activations of the plurality ofneighboring cardiac signals to indicate a source of a cardiac rhythmdisorder.

In various embodiments or aspects, the heart rhythm disorder can betreated using the constructed clinical representation.

These and other purposes, goals and advantages of the presentapplication will become apparent from the following detailed descriptionread in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments or aspects are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which:

FIG. 1 illustrates an example cardiac activation reconstruction system;

FIG. 2 illustrates an example simple electrogram signal of a heartrhythm disorder from a sensor positioned at a sensor location in a heartillustrated in FIG. 1;

FIG. 3 illustrates an example complex electrogram signal of a heartrhythm disorder from a sensor positioned at a sensor location in a heartillustrated in FIG. 1;

FIG. 4 illustrates an example array of sensors of a catheter illustratedin FIG. 1 and an example selection of signals from the sensors toreconstruct cardiac activation information;

FIG. 5 illustrates example comparison pairs of signals from the sensorsof the array illustrated in FIG. 4;

FIG. 6 is an illustration of an example signal pair comparison ofanalysis signal (SIG1) and reference signal (SIG2);

FIG. 7 is an illustration of another example signal pair comparison ofanalysis signal (SIG1) and reference signal (SIG2);

FIG. 8 is an illustration of a further example signal pair comparison ofanalysis signal (SIG1) and reference signal (SIG2) utilizing a compositesignal;

FIG. 9 is a flowchart that illustrates an example method ofreconstructing cardiac activation information associated with heartrhythm disorders;

FIG. 10 is an illustration of an example signal pair comparison ofanalysis signal (SIG1) and reference signal (SIG2) that can be processedin accordance with the method of FIG. 9 to reconstruct cardiacactivation information;

FIG. 11 is an illustration of an example mapping of processed signals inaccordance with FIGS. 1-10; and

FIG. 12 is a block diagram of an illustrative embodiment of a generalcomputer system.

DETAILED DESCRIPTION

A system and method for reconstructing cardiac activation informationassociated with heart rhythm disorders are disclosed herein. In thefollowing description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of example embodiments or aspects. It will be evident,however, to one skilled in the art, that an example embodiment may bepracticed without all of the disclosed specific details.

FIG. 1 illustrates an example cardiac activation reconstruction system100. The example system 100 is configured to detect and reconstructcardiac activation information collected/detected from a patient's heartin connection with a heart rhythm disorder. The heart includes a rightatrium 122, left atrium 124, right ventricle 126 and left ventricle 128.

The example system 100 includes a catheter 102, signal processing device114, computing device 116 and analysis database 118.

The catheter 102 is configured to detect cardiac activation informationin the heart and to transmit the detected cardiac activation informationto the signal processing device 114, either via a wireless or wiredconnection. The catheter includes a plurality of probes/sensors 104-112,which can be inserted into the heart through the patient's bloodvessels.

In some embodiments or aspects, one or more of the sensors 104-112 arenot inserted into the patient's heart. For example, some sensors maydetect cardiac activation via the patient's surface (e.g.,electrocardiogram) or remotely without contact with the patient (e.g.,magnetocardiogram). As another example, some sensors may also derivecardiac activation information from cardiac motion of a non-electricalsensing device (e.g., echocardiogram). In various embodiments oraspects, these sensors can be used separately or in differentcombinations, and further these separate or different combinations canalso be used in combination with sensors inserted into the patient'sheart.

The sensors 104-112, which are positioned at sensor locations in theheart under consideration, can detect cardiac activation information atthe sensor locations and can further deliver energy to ablate the heartat the sensor locations. It is noted that the sensors 104-112 can alsodetect cardiac activation information from overlapping regions of theheart (e.g., right atrium 122 and left atrium 124).

The signal processing device 114 is configured to process (e.g., clarifyand amplify) the cardiac activation information detected by the sensors104-112 at the sensor locations into electrogram signals and to providethe processed cardiac signals to the computing device 116 for analysisor processing in accordance with methods disclosed herein. In processingthe cardiac activation information from the sensors 104-112, the signalprocessing device 114 can subtract cardiac activation information fromoverlapping regions of the heart 120 to provide processed cardiacsignals to the computing device 116 for analysis. While in someembodiments or aspects, the signal processing device 114 is configuredto provide unipolar signals, in other embodiments or aspects, the signalprocessing device 114 can provide bipolar signals.

The computing device 116 is configured to receive (or access) cardiacsignals from the signal processing device 114 and further configured toanalyze or process the cardiac signals in accordance with methods,functions or logic disclosed herein to reconstruct cardiac activationinformation in the cardiac signals such that it is possible to locate acause of the heart rhythm disorder and to eliminate the cause.

For example, the computing device 116 can process a first cardiac signaland a second cardiac signal from the received cardiac signals todetermine whether there is a point of change in a derivative of thefirst cardiac signal with respect to a derivative of the second cardiacsignal above a threshold. The computing device 116 can then assign anactivation onset time in the first signal at the point of change todefine cardiac activation indicating a beat in the first signal if it isdetermined that the point of change is above the threshold.

As another example, the computing device 116 can iteratively selectpairs of cardiac signals from the received cardiac signals, each pairhaving a first cardiac signal and second cardiac signal. The computingdevice 116 can process and assign for each of the pairs in order todefine multiple cardiac activations indicating beats for the firstcardiac signal in each of the pairs. For example, the computing device116 is configured to perform processing and assigning to define multiplecardiac activations indicating beats in the first cardiac signal. Thecomputing device 116 can then reconstruct a cardiac activation patternbased on assigned activation onset times of cardiac activations from thereceived cardiac signals to indicate a source of a rhythm disorder. Insome embodiments or aspects, the computing device 116 can also displaythe reconstructed cardiac activation pattern to facilitate treatment ofcardiac tissue at the source to suppress, lessen or eliminate thecardiac rhythm disorder.

The analysis database 118 is configured to support or aid in theanalysis of the signals by the computing device 116. In some embodimentsor aspects, the analysis database 118 can store a catalog of referencesignals and associated activations to enable the computing device 116 todetermine an activation onset associated with a signal being considered(e.g., when point of change is below threshold during a time window), aswill be described in greater detail herein.

FIG. 2 illustrates an example simple electrogram signal 200 of a heartrhythm disorder from a sensor positioned at a sensor location in theheart 120. For example, sensor 104 of catheter 102 can be positioned ata sensor location in the right atrium 122, as shown in FIG. 1. As anexample, the heart rhythm disorder can be a complex rhythm disorder AF,VF and polymorphic VT, or another heart rhythm disorder.

The example signal 200 is for a time period between about 300 ms andabout 900 ms. During this time period, the signal 200 is expected tohave four (4) local activation onsets 204-208, e.g., those activationonsets that originate at or near (locally to) the sensor location in theheart 120 of sensor 104. Specifically, based on established observationsin heart rhythm disorders, cycle length between activation onsets ofabout 100 ms to about 300 ms can be expected for AF, and cycle lengthbetween activation onsets of about 180 ms to about 240 ms can beexpected for complex ventricular arrhythmias. As an example, cyclelength 210 of about 100 ms to about 300 is expected between activationonset 202 and activation onset 204. In the example signal 200, theactivation onsets 204-208 are generally identifiable as having a smalldegree of baseline wander superposed in the local signal with fewfar-field artifacts that could be mistaken as local activity. Localactivity in this example can be characterized by an activation onsetwith a sharp inflection point and high slope, followed by a period ofgentle, low-deviation slope representing repolarization, typicallylasting between about 100 ms and 250 ms.

In the example signal 200, an example far-field deflection 212 isillustrated between location activation onset 206 and local activationonset 208, e.g., an activation onset that originates at a location inthe heart 120 that is different than the sensor location associated withthe sensor 104. Specifically, the heart 120 at the sensor locationassociated with sensor 104 cannot physiologically activate again afteractivation onset 206 in a shorter cycle than about 100 ms to about 300ms because local tissue must undergo repolarization. Moreover, thedeflection 212 cannot be local to the sensor location associated withthe sensor 104 when the deflection 212 is also significantly present insignals collected by neighbor sensors in multiple directions to sensor104. For example, the far-field deflection 212 detected by sensor 104can be associated with activation onset at a sensor location associatedwith sensor 106.

FIG. 3 illustrates an example complex electrogram signal 300 of a heartrhythm disorder from a sensor positioned at a sensor location in theheart 120. For example, sensor 106 of catheter 102 can be positioned ata sensor location in the right atrium 122, as shown in FIG. 1. As anexample, the heart rhythm disorder can be a complex rhythm disorder AF,VF and polymorphic VT, or another heart rhythm disorder.

Similarly to example signal 200, example signal 300 is for a time periodbetween about 300 ms and about 900 ms. During this time period, thesignal 300 is expected to have four (4) local activation onsets, e.g.,activation onsets that originate locally to the sensor location in theheart 120 of sensor 106. However, in the example signal 300 there areeleven (11) possible activation onsets 302-322. Multiple deflections ofshort duration (shorter than shortest cycle length of about 100 ms)caused by the heart rhythm disorder makes the discernment of localactivation onsets at the sensor location of sensor 104 as opposed tofar-field activations or simply noise prohibitively difficult.

FIG. 4 illustrates an example array of sensors 400 of catheter 102 andan example selection of signals from the sensors to reconstruct cardiacactivation information (e.g., activation onsets). The array 400 includesfifteen (15) example sensors for simplicity and clarity of thedescription. It is to be understood that the array 400 can include feweror more sensors to as may be determined to cover different portions ofthe heart 120. In some embodiments or aspects, the array 400 can include160 or more sensors.

The sensors of the array 400 are shown in example spatial arrangementwith respect to the right atrium 122 of the heart 120. Similarly, thearray 400 can be spatially arranged in other chambers of the heart,e.g., left atrium, right ventricle, left ventricle, or for combinationsof chambers including the endocardial or epicardial surfaces. In FIG. 4,the spatial arrangement of electrodes in the array 400 is shown to beuniform and planar for simplicity and clarity of the description.However, the heart 120 is not a uniform or planar structure.Accordingly, the spatial arrangement of electrodes in the array 400 canbe varied with respect to the shape of the heart 120 to improvedetection of electric activity in the heart 120.

In one example embodiment or aspect, catheter 102 of FIG. 1 can be abasket catheter with the example sensors of the array 400 disposed inspatial arrangements along splines 406-408 of the basket catheter 102.Different catheters with various spatial arrangements of the sensors inthe sensor array 400 can be used, such as spiral, radial spokes or otherspatial arrangements.

Pairs of sensors (signals of sensors) in the array 400 are iterativelyselected for processing as will be described in greater detail herein inorder to reconstruct cardiac activation information (activation onsets)of the heart 120 in the right atrium 122, or another chamber in whichthe array 400 may be disposed.

As illustrated at 402, an analysis signal (1) is selected forprocessing. A reference signal (2)—a neighbor to the analysis signal(1)—is then selected to form a first pair that is processed to determineactivation onsets in the analysis signal (1). Similarly, as illustratedat 404, an analysis signal (1) is selected for processing. A referencesignal (2)—another neighbor to the analysis signal (1)—is then selectedto form a second pair that is processed to determine activation onsetsin the analysis signal (1). The activation onsets from the first pairand the second pair of signals can be stored in memory of computingdevice 116 or database 118 of FIG. 1. The neighboring sensors (signals)can but do not have to be adjacent, as will be described in greaterdetail below.

The selections and processing are repeated for the sensors of the array400 (signals) that neighbor the analysis signal (1). The activationonsets in the analysis signal (1) for all pairs of signals can also bestored in memory of computing device 116 or database 118. Thereafter,another analysis signal is selected and the selections and processingare repeated for that analysis signal. In this fashion, each of theplurality of analysis signals in array 400 is processed against itsneighboring signals. The number of neighboring signals for a givenanalysis signal can be fewer or greater depending on the spatialarrangement of the sensors in the array 400, the chamber of the heartanalyzed and the heart rhythm disorder treated.

FIG. 5 illustrates example comparison pairs of signals from the sensorsof the array 400 illustrated in FIG. 4. Neighbor signals can include notonly those signals that are immediately adjacent to the analysis signalbut also those signals not adjacent to the analysis signal. Spatiallyseparating the paired sensors can have the effect of spatially extendingthe area over which deflections are considered to be local activity.Local activity is therefore approximately defined by the separation ofthe paired sensors. As illustrated in example 1 of FIG. 5, selectedanalysis signal (1) is processed against adjacent signals (2)-(5) andalso against a non-adjacent signal (6). As further illustrated inexample 2 of FIG. 5, selected analysis signal (1) is processed againstadjacent signals (2)-(5) and also against a non-adjacent signals (6) and(7). While closest neighbor signals are preferred, neighbor signals invarious spatial orientations with respect to the analysis signal can beused.

For each analysis signal, there could be a plurality of referencesignals (e.g., four (4) reference signals or greater). A finalactivation onset in the analysis signal is determined with reference toor based on the combination of the reference signals' possibleactivation onsets. Specifically, the activation onsets determined fromeach pair can be referenced against each other to check forcorrespondence or association of activations in the analysis signal. Anactivation onset for the analysis signal is finalized based on thepossible activation onsets of the referenced pairs of signals.

The final activation onset for the analysis signal can be determined invarious ways. In one embodiment or aspect, the final activation onsetfor the analysis signal can be determined based on an average of thepossible activation onsets from the various pairs of referenced signals.In another embodiment or aspect, the final activation onset for theanalysis signal can be determined based on an average of the possibleactivation onsets from those pairs of signals in which a majority of thepossible activation onsets are within a predetermined time interval ofeach other (e.g., ±5 ms). The time interval used can be chosen to belower or higher. Alternatively, the final activation can also bedetermined by performing a “center-of-mass” calculation weighted by thesignificance value of each of the possible activation onsets in themajority, or by analysis of a predominant direction of activation onsetsrelative to sensor locations.

With reference to example 1 in FIG. 5, if an analysis signal has beendetermined to have possible activation onsets of 170 ms, 190 ms, 193 ms,165 ms and 172 ms in connection with the five (5) reference signalpairs, respectively, then the final activation onset for the analysissignal can be determined to be (170+165+172)/3=169 ms. The activationonsets of 190 ms and 193 ms that are outside the time interval can bediscounted from the determination of the final activation onset for theanalysis signal. The final activation onset determined for each signalcan be saved in the database 118 of FIG. 1.

While in the forgoing examples for the sake of brevity and clarity, onlyone activation onset was determined for the analysis signal inconnection with each reference signal, it should understood that eachsignal (from a sensor of array 400) can represent multiple successiveanalysis intervals (e.g., activation cycles) as illustrated in FIG. 2,each of which can have an activation onset as determined based on thesame time interval of multiple reference signals (neighboring sensors ofarray 400).

FIG. 6 is an illustration of an example signal pair comparison 600 ofexample analysis signal (SIG1) and example reference signal (SIG2). Forexample, the signals can be from comparison pair 402 (or comparison pair404) illustrated in FIG. 4, or from any comparison pair illustrated inFIG. 5. It is noted that the signals are illustrative and occur duringthe same analysis interval. As noted herein, the signals can havemultiple successive analysis intervals (e.g., activation cycles), asillustrated in FIG. 2.

The signals are processed at one or more successive time points (e.g.,every millisecond, two milliseconds, or other time points) to determinewhether there is a point of change in a derivative of the analysissignal with respect to a derivative of the reference signal above athreshold. The point of change can be determined from one or more ofslope, amplitude, timing and shape for the first cardiac signal and thesecond cardiac signal. It is noted that in some embodiments or aspects,processing of some time points can be omitted (e.g., every other timepoint or two of three time points). A first derivative (or secondderivative can be used) is determined for each of the time points in thesignals. A root mean squared is determined for each of the signals. Forexample, RMS1 and RMS2 are determined by taking a root mean squared ofthe derivatives for the entire signal of each of the signals (e.g., allactivation cycles). RMS can be used to normalize the amplitude of thesignals with respect to one another, such that amplitudes (e.g.,voltage) of the deflections in the signals do not affect the processingof the signals as described below.

A time point (same time point or about the same time point) issuccessively selected from each of the signals (SIG1, SIG2) forconsideration and processing. For each time point under consideration, atime increment 602, 604 in each signal starting at that time point canbe considered. For example, a time increment of 10 ms can be used.Different time increments can be selected. A line which is pinned to thepoint under consideration in each signal and which provides the best fitto the time points in the time increment of each signal is determined.The determined lines represent the slopes (e.g., volts/per second) ofthe signals for the selected time point. More specifically, thedetermined lines represent slopes of the signals at the selected timepoint for the same time increment (e.g., 10 ms). A significance value(δ) is determined with respect to the slopes.

The significance value can be determined by taking an absolute value ofthe first slope over its associated root mean squared value andsubtracting an absolute value of the second slope over its associatedroot mean squared value. A determination is made as to whether theresulting (δ)=−0.461 is above a significance threshold (e.g., 0.25). Thesignificance threshold indicates that there is a potentially significantpoint of change (based on slopes) for the time point in the signalsunder consideration, e.g., that the derivatives diverge sufficientlyfrom each other. In the example signal pair comparison 600, thesignificance value (δ)=−0.461 is below the significance threshold of0.25. According to the foregoing divergence criteria, the lowsignificance value indicates that the deflection in SIG1 is far-fieldand not sufficiently local to a sensor location from which the signaloriginated, e.g., a sensor shown in FIG. 4. Accordingly, there is nopotentially significant point of change in the example signal paircomparison 600.

Other characteristics of the signals under consideration can also beused to determine divergence criteria in order to separate localactivation from far-field activation if one or more of the criteriaexceed a significance threshold. These and other divergence criteria canbe applied independently, or in combination.

A first characteristic is voltage (or amplitude) of the signal, in whichvoltage exceeding a significance threshold indicates local activationrather than far-field activation. The significance threshold for avoltage (or amplitude) varies in the presence of structural disease orscar which may reduce voltage even at locally-activated sites, poorelectrode contact which may also reduce voltage even atlocally-activated sites, larger electrical sensors which will altervoltage depending on signal properties within larger sensed areas,signal filtering which may attenuate high or low voltage events iftransient, and other factors.

A second characteristic is cycle length (CL), in that far-field signalsmay have a CL that differs by a greater than significance threshold fromlocal signals in a complex rhythm disorder, such as atrial orventricular fibrillation. This significance threshold will differ basedon the rhythm under consideration, the heart chamber underconsideration, and properties such as action potential duration (APD),conduction velocity (CV), the presence of structural disease or whetherthese sites are situated parallel to or perpendicular to fiberorientations.

A third characteristic is slope of the signal (upstroke or downstroke;dV/dt)—that is, the rate at which the signal amplitude changes from abaseline to indicate a potential beat. Local activation at a sitecompared to another site indicates local activation at the first site.This third characteristic will be sensitive to factors that reduce dV/dtsuch as poor electrode contact, conduction slowing (intrinsic or due tometabolic abnormalities or drugs), structural disease, or to factorsthat increase dV/dt. Slopes that are very steep, such as >1 my/40 ms,are clearly associated with local activation, but other slopes may alsoindicate local activation depending upon other factors.

A fourth characteristic is frequency content (or the square of thefrequency; energy)—in which local signals have higher frequency (higherenergy) than far-field signals. The significance threshold will varywith signal filtering settings, sensor properties (such as size), andcontact with the tissue. Notably, signals obtained through insulatorsmay have attenuation of certain frequencies, for example, signalsdetected from the esophagus or body surface have traveled through moretissue that may attenuate high frequency signals compared to signalsobtained directly from the heart, and this will alter the signalproperties. Situations may arise in which intervening tissue (such asbone) or a device (a signal amplifier) may amplify certain frequencies.

Additionally, repeatability of any of the above criteria over time maybe used to determine divergence criteria in order to separate localactivation from far-field activation, since in complex rhythm disorders,local activation may remain more consistent than far-field activationwhich may vary. Repeatability can be measured using correlation values,indexes of disorder such as Shannon entropy, differential entropy,Kolmogorov complexity, and/or other measures of entropy.

As noted herein, the signals can have multiple successive analysisintervals (e.g., activation cycles), as illustrated in FIG. 2. In eachanalysis interval, it is possible to have zero, one or more potentiallysignificant points of change as described above. The time point underconsideration and the potentially significant point(s) of change can berecorded, such as in database 118.

FIG. 7 is an illustration of an example signal pair comparison 700 ofexample analysis signal (SIG1) and example reference signal (SIG2).Similarly, the signals can be from comparison pair 402 (or comparisonpair 404) illustrated in FIG. 4, or from any comparison pair illustratedin FIG. 5. The signals are illustrative and occur during the sameanalysis interval. As noted herein, the signals can have multiplesuccessive analysis intervals (e.g., activation cycles), as illustratedin FIG. 2.

The signals are processed at one or more successive time points todetermine whether there is a point of change in a derivative of theanalysis signal with respect to a derivative of the reference signalabove a threshold. In some embodiments or aspects, processing of sometime points can be omitted (e.g., every other time point or two of threetime points). A first derivative (or second derivative) is determinedfor each of the time points in the signals. A root mean squared isfurther determined for each of the signals. A time point (same timepoint or about the same time point) is successively selected from eachof the signals (SIG1, SIG2) for consideration and processing. For eachtime point under consideration, a time increment 702, 704 (e.g., 10 ms)in each signal starting at that time point can be considered. A linewhich is pinned to the point under consideration in each signal andwhich provides the best fit to the time points in the time increment ofeach signal is determined. The determined lines represent the slopes(e.g., volts/per second) of the signals for the selected time point.More specifically, the determined lines represent the slopes at theselected time point for the same time increment. A significance value(δ) is determined with respect to the slopes.

The significance value can be determined by taking an absolute value ofthe first slope over its associated root mean squared value andsubtracting an absolute value of the second slope over its associatedroot mean squared value. A determination is made as to whether theresulting (δ)=−0.063 is above a significance threshold (e.g., 0.25). Inthe example signal pair comparison 700, the significance value(δ)=−0.063 is well below the significance threshold of 0.25. The lowsignificance value indicates low amplitude noise. Accordingly, there isno potentially significant point of change in the example signal paircomparison 700.

A noise level can be defined as fraction of the significance thresholdor can be defined programmatically in various ways. For example, noiselevel can be one-tenth (0.025) of the significance threshold (0.25). Adifferent fraction level can be selected. As another example, the noiselevel can be defined as a Gaussian standard deviation of a plurality ofsignificance values. Other ways of defining the noise level arecontemplated. It is noted that the significance threshold (e.g., 0.25)is higher than the noise level that can be associated with the analysissignal and reference signal in the example signal pair comparison 700.Accordingly, a point of change at or below noise level can be associatedwith one or more signals from other regions of a heart, respiratorysystem, gastrointestinal tract, neurological system as well aselectronic interference.

As noted herein, the signals can have multiple successive analysisintervals (e.g., activation cycles) and in each analysis interval, it ispossible to have zero, one or more potentially significant points ofchange as described above. The time point under consideration and thepotentially significant point(s) of change can be recorded, such as indatabase 118.

FIG. 8 is an illustration of an example signal pair comparison 800 ofexample analysis signal (SIG1) and example reference signal (SIG2)utilizing a composite signal. As in the other examples, the signals canbe from comparison pair 402 (or comparison pair 404) illustrated in FIG.4, or from any comparison pair illustrated in FIG. 5. The signals areillustrative and occur during the same analysis interval. As notedherein, the signals can have multiple successive analysis intervals(e.g., activation cycles), as illustrated in FIG. 2.

The signals are processed at one or more successive time points todetermine whether there is a point of change in a derivative of theanalysis signal with respect to a derivative of the reference signalabove a threshold. In some embodiments or aspects, processing of sometime points can be omitted (e.g., every other time point or two of threetime points). A first derivative (zero order derivative or secondderivative) is determined for each of the time points in the signals. Aroot mean squared is further determined for each of the signals. A timepoint (same time point or about the same time point) is successivelyselected from each of the signals (SIG1, SIG2) for consideration andprocessing. For each time point under consideration, a time increment802, 804 (e.g., 10 ms) in each signal starting at that time point can beused. A line which is pinned to the point under consideration in eachsignal and which provides the best fit to the time points in the timeincrement of each signal is determined. The determined lines representthe slopes (e.g., volts/per second) of the signals for the selected timepoint. More specifically, the determined lines represent the slopes ofthe signals at the selected time point for same time increment. Asignificance value (δ) is determined with respect to the slopes.

In some embodiments or aspects, the significance value can be determinedby taking an absolute value of the first slope over its associated rootmean squared value and subtracting an absolute value of the second slopeover its associated root mean squared value. A determination is made asto whether the resulting (δ)=0.546 is above a significance threshold(e.g., 0.25). In the example signal pair comparison 800, thesignificance value (δ)=0.546 is determined to be above the significancethreshold of 0.25.

Accordingly, there is a potentially significant point of change in theexample signal pair comparison 800 at the time point underconsideration. As noted herein, the signals can have multiple successiveanalysis intervals (e.g., activation cycles) and in each analysisinterval, it is possible to have zero, one or more potentiallysignificant points of change as described above. The time point underconsideration and the potentially significant point(s) of change can berecorded, such as in database 118.

In other embodiments or aspects, the significance value can bedetermined with respect to a composite signal. Specifically, a compositesignal (COMP) is computed by subtracting SIG2 (reference signal) fromSIG1 (analysis signal), e.g., COMP=SIG2−SIG1. The composite signal canrepresent a bipolar signal (COMP) of constituent unipolar signals (SIG1,SIG2). In alternate embodiments or aspects, the composite signal COMPcan also be computed by adding signals SIG1 and SIG2. The signals in thesignal pair comparison 800 are illustrative and occur during the sameanalysis interval. As noted herein, the signals can have multiplesuccessive analysis intervals (e.g., activation cycles), as illustratedin FIG. 2.

The signals SIG1, SIG2 are processed at one or more successive timepoints with respect to the composite signal COMP to determine whetherthere is a point of change in a derivative of the analysis signal withrespect to a derivative of the reference signal above a threshold. Afirst derivative (or second derivative) is determined for each of thetime points in the signals, SIG1, SIG2, COMP. A time point (same timepoint or about the same time point) is successively selected from eachof the signals (SIG1, SIG2, COMP) for consideration and processing. Foreach time point under consideration, a time increment 802, 804, 806(e.g., 10 ms) in each signal starting at that time point can beconsidered. A line which is pinned to the point under consideration ineach signal and which provides the best fit to the time points in thetime increment of each signal is determined. The determined linesrepresent the slopes (e.g., volts/per second) of the signals for theselected time point. More specifically, the determined lines representthe slopes of the signals at the selected time point for the same timeincrement. A significance value (δ) is determined with respect to theslopes.

In the embodiments or aspects employing the composite signal, thesignificance value (δ) can be determined by a ratio taking an absolutevalue of the second slope and subtracting an absolute value of thecomposite slope, and dividing by a logarithm of a result of an absolutevalue the first slope subtracting an absolute value of the compositeslope. The resulting significance value for the time point underconsideration is (δ)=31.63. Significance values can be computed for allpoints under consideration. A significance threshold can be determinedto be an average of the computed significance values (δ) plus a standarddeviation. Thereafter, only those significance values (δ) that are abovethe significance threshold can be considered to be potentiallysignificant points of change for the comparison pair 800. For theexample signals in the signal pair comparison 800 of FIG. 8, thedetermined significance threshold can be 10. It is noted that thesignificance value(s) that is above the significance threshold generallyextends substantially above the significance threshold. For example, asignificance value (δ)—having the largest ratio—can therefore beselected.

Accordingly, there is a potentially significant point of change in theexample signal pair comparison 800 at the time point underconsideration. As noted herein, the signals can have multiple successiveanalysis intervals (e.g., activation cycles) and in each analysisinterval, it is possible to have zero, one or more potentiallysignificant points of change as described above. The time point underconsideration and the potentially significant point(s) of change can berecorded, such as in database 118.

FIG. 9 is a flowchart that illustrates an example method 900 ofreconstructing cardiac activation information (activation onset)associated with heart rhythm disorders. The example method 900 can beperformed by the computing device 116 illustrated in FIG. 1. Morespecifically, the example method 900 starts at operation 902 at whichsignals are received by the computing device 116 via signal processingdevice 114 from sensors disposed in the heart 120. For example, signalscan be received from sensors of the sensor array 400 disposed in theright atrium 122 of the heart 120, as shown in FIGS. 1 and 4. In someembodiments or aspects, at least a portion of the signals from thesensors can be recorded by signal processing device 114 and thenprovided to computing device 116.

At operation 904, a first signal (analysis signal) is selected. Atoperation 906, a second signal (reference signal) is selected. Selectionof the analysis signal and the reference signal can be performed asdescribed in greater detail with reference to FIGS. 4 and 5. In someembodiments or aspects, a root mean squared (RMS) can be determined forthe first signal and for the second signal. At operation 908, a timeinterval over which the first signal and the second signal are to becompared is selected. The time interval can be selected to be anactivation cycle (e.g., 100 ms to 300 ms) as described in FIG. 2. Insome embodiments or aspects, the time interval can be determined by adominant frequency analysis or other analysis of the average cyclelength of the first (analysis) signal. A default time interval of 200 mscan be used if the time interval cannot be determined computationally.In other embodiments or aspects, the time interval can be selectedmanually, computationally by a different analysis method, from adatabase that catalogs such time intervals for patients of a certainage, gender and type of heart rhythm disorder, or defaulted to a valuebetween about 100 ms and about 300 ms.

In some embodiments or aspects, a composite signal can be determinedbased on the selected first signal and the second signal, such as bysubtracting or adding the signals as described with reference to FIG. 8.

At operation 910, a time point is selected for consideration in theselected time interval. The same or about the same time point isselected for consideration in each signal (e.g., first signal and secondsignal). At operation 912, derivatives are calculated for a timeincrement (e.g., 10 ms) extending from the point of consideration ineach signal. In those embodiments or aspects that use a compositesignal, a derivative is also calculated for a time increment (e.g., 10ms) extending from a time point of consideration in the compositesignal. The time point of consideration in the composite signal is thesame or about the same as in the other signals (e.g., first signal andsecond signal).

At operation 914, a determination is made as to whether all points inthe selected time interval have been processed. If it is determined thatall point in the selected time interval were processed, the method 900continues at operation 916. Alternatively, the method 900 performsoperations 910, 912 until all points in the selected time interval aredetermined to be processed at operation 914.

At operation 916, points of change between the derivatives of the firstsignal with respect to the derivatives of the second signal aredetermined in the time interval under consideration. For example, asignificance value (δ) can be determined at each point of change asdescribed with reference to FIGS. 6-8.

At operation 918, a determination is made as to whether there is apoint(s) of change in the derivative of the first cardiac signal withrespect to the derivative of the second cardiac signal above athreshold. For example, it can be determined whether the significancevalue (δ) at the point of change is above the threshold. In someembodiments or aspects that do not use a composite signal, the thresholdcan be 0.25 (or another value) as described with reference to FIGS. 6-8,while in those embodiments or aspects that use a composite signal, thethreshold can be computed as an average value plus a standard deviationof all points of change as described with reference to FIG. 8.

If it is determined that there is a point(s) of change above thethreshold, the method 900 continues at operation 920 where thesignificant point(s) of change is recorded (selected) as a possibleactivation onset(s) for the time interval under consideration in thefirst (analysis) signal. If however, it is determined that there is nopoint of change above the threshold (no significant point of change),the method 900 continues at operation 924 where the first signal iscompared over the time interval to a catalog of reference signals. Forexample, the catalog of reference signals for heart rhythm disorders canbe maintained in database 118. At operation 926, a determination is madeas to whether there is a match to a reference signal in the database.The comparison can be based on at least one characteristic of the firstsignal to at least one characteristic of the reference signal, such asshape, slope, amplitude, frequency and/or timing. Other characteristicscan be used together with or instead of the enumerated characteristics.

If there is no match to a reference signal at operation 926, the method900 continues at operation 922. Alternatively, the method 900 continuesat operation 928 where the point(s) of change in the time interval underconsideration is recorded (selected), which would correspond toactivation onset(s) in the reference signal that was matched.

At operation 922, a determination is made as to whether all timeintervals in the signals have been processed. If it is determined thatall time intervals have not been processed, the method 900 continues toperform operations 908-922 to process subsequent time intervals until itis determined that all time intervals have been processed. Thesubsequent time interval can be determined from the point(s) of changethat represents the possible activation onset at 920. Specifically, ifonly one point of change (above the threshold) is recorded at 920, thenthe next time interval (e.g., 100 ms to 300 ms) starts at the onset timeassociated with the point of change plus a half of a cycle length (e.g.,50 ms to 150 ms). If there are multiple points of change, then the onsettime associated with the largest point of change (significance value) isused to determine the next time interval for operations 908-922. It isnoted that the determination of the next time interval can be extendedto consider significant points of change from all second (reference)signals for the same time interval under consideration. However, if itis determined that all time intervals have been processed at operation922, the method 900 continues at operation 930.

At operation 930, a determination is made as to whether all second(reference) signals have been processed in association with the selectedfirst (analysis signal). If it is determined that all second signalshave not been processed, the method 900 continues to perform operations906-930 until it is determined that all second (reference) signals havebeen processed for the first (analysis) signal. However, if it isdetermined that all second signals have been processed, the method 900continues to operation 932.

At operation 932, an activation onset(s) is assigned in the first signalat the point(s) of change to define cardiac activation(s) indicating abeat(s) in the first signal if it is determined (at operation 918) thatthe point(s) of change is above the threshold. Similarly, at operation932 an activation onset(s) can be assigned in the first signal at thepoint(s) of change to define cardiac activation(s) indicating a beat(s)in the first signal based on a matched reference signal (at operation928). More specifically, activation onsets are assigned to the timeintervals of the first signal based on the recorded (or significant)point(s) of change of the first signal with reference to the secondsignal(s). That is, an activation onset is assigned to each timeinterval in the first (analysis) signal based possible activationonset(s) associated with the significant point(s) of change in the sametime interval of the second (reference) signal(s). As described withreference to FIG. 5, the activation onset for the time interval of thefirst (analysis) signal can be determined based on an average of theactivation onsets with reference to the second (reference) signals. Inanother embodiment or aspect, the activation onset for the time intervalof the first signal can be determined based on an average of activationonsets with reference to those second signals in which a majority ofactivation onsets are within a predetermined time interval of each other(e.g., ±5 ms). The assigned onset can be recorded for each interval inthe first (analysis) signal such as in database 118.

At operation 934, a determination is made as to whether all signals havebeen processed or analyzed as first (analysis) signals against second(reference) signals. If it is determined that all signals have not beenprocessed, then the method 900 continues to perform operations 904-932until all signals have been processed. Alternatively, if it isdetermined that all signals have been processed, the method 900 ends atoperation 936.

At the conclusion of the method 900, signals collected from the heart120 have been reconstructed with cardiac activation information(activation onsets) such that a cause of the heart rhythm disorder canbe determined. More specifically, unipolar electrograms or monophasicaction potentials (MAPs) can be mapped to the reconstructed activationonsets of the signals to show unipolar or MAP sequences orrepresentations for the signals. An activation map or pattern can beconstructed from these unipolar voltage or MAP voltage representationsof the signals to locate the cause of the heart rhythm disorder. Anexample MAP representation and example activation map are illustrated inFIG. 11.

FIG. 10 is an illustration of an example signal pair comparison 1000 ofanalysis signal (SIG1) and reference signal (SIG2) that can be processedin accordance with method 900 of FIG. 9 to assign an activation onset1004. As illustrated in comparison 1000, a time interval 1002 (e.g., 100ms-300 ms) is selected for comparison and processing. In some exampleembodiments or aspects, the signals in the time interval (SIG1, SIG2,COMP) are smoothed, such as via median filter. Significance values (δ)are determined for the points of changes in the signals' first or secondderivative, as described herein with reference to FIGS. 1-9. Asillustrated in signal pair comparison 1000, point of change 1012 in SIG1that is above threshold 1010 is assigned as the activation onset 1004for the time interval 1002 in SIG1 based on the first derivative.Alternatively, point of change 1014 in SIG 1 that is above threshold1010 is assigned as the activation onset 1004 for the time interval 1002in SIG1 based on the second derivative. Subsequent time intervals areselected and activation onsets are assigned as described herein withreference to FIGS. 1-9 until the analysis signal (SIG1) is processed.

FIG. 11 is an illustration of an example mapping 1100 of processedsignals in accordance with FIGS. 1-10. Raw signal 1100 represents asignal that is processed to assign activation onsets (vertical lines) asdescribed herein. For reference purposes, a composite signal 1102 isshown, which results from the raw (analysis) signal 1100 and another(reference) signal (not shown). A monophasic action potential (MAP)voltage representation is generated from each processed signal 1100.Multiple signals are processed as described herein and MAPs generatedbased on the processed signals. The electrical activity of all MAPs ismapped in a sequence of example activation mappings 1106 to showactivation onsets 1108, 1110, 1112 and 1114 at each time interval,respectively. These mappings can be displayed by computing device 116.Although only four mapping are shown for illustrative purposes, therecan be fewer or greater number of mappings 1106 based on the timeintervals represented in the signals.

As shown by the arrows in the example mappings 1106 (e.g., activationonsets 1108-1114), the electrical activity indicates a rotationalactivation pattern of activation onsets (rotor) in the heart rhythmdisorder. At least a portion of the area of the heart 120 indicated bythe rotational activation pattern indicated by the arrows in FIG. 11 canbe treated to eliminate the cause of the heart rhythm disorder, andtherefore the heart rhythm disorder itself. Such treatment may bedelivered by ablation using various energy sources (including but notlimited to radiofrequency, cryoenergy, microwave, and ultrasound), genetherapy, stem cell therapy, pacing stimulation, drug or other therapy.It is noted that the MAP representation and activation map are examplesto illustrate a rotational activation pattern. Other activation patternscan result from different example signals collected by the sensors fromthe heart 120.

FIG. 12 is a block diagram of an illustrative embodiment of a generalcomputer system 1200. The computer system 1200 can be the signalprocessing device 114 and the computing device 116 of FIG. 1. Thecomputer system 1200 can include a set of instructions that can beexecuted to cause the computer system 12800 to perform any one or moreof the methods or computer based functions disclosed herein. Thecomputer system 1200, or any portion thereof, may operate as astandalone device or may be connected, e.g., using a network or otherconnection, to other computer systems or peripheral devices. Forexample, the computer system 1200 may be operatively connected to signalprocessing device 114 and analysis database 118.

The computer system 1200 may also be implemented as or incorporated intovarious devices, such as a personal computer (PC), a tablet PC, apersonal digital assistant (PDA), a mobile device, a palmtop computer, alaptop computer, a desktop computer, a communications device, a controlsystem, a web appliance, or any other machine capable of executing a setof instructions (sequentially or otherwise) that specify actions to betaken by that machine. Further, while a single computer system 1200 isillustrated, the term “system” shall also be taken to include anycollection of systems or sub-systems that individually or jointlyexecute a set, or multiple sets, of instructions to perform one or morecomputer functions.

As illustrated in FIG. 12, the computer system 1200 may include aprocessor 1202, e.g., a central processing unit (CPU), agraphics-processing unit (GPU), or both. Moreover, the computer system1200 may include a main memory 1204 and a static memory 1206 that cancommunicate with each other via a bus 1226. As shown, the computersystem 1200 may further include a video display unit 1210, such as aliquid crystal display (LCD), an organic light emitting diode (OLED), aflat panel display, a solid state display, or a cathode ray tube (CRT).Additionally, the computer system 1200 may include an input device 1212,such as a keyboard, and a cursor control device 1214, such as a mouse.The computer system 1200 can also include a disk drive unit 1216, asignal generation device 1222, such as a speaker or remote control, anda network interface device 1208.

In a particular embodiment or aspect, as depicted in FIG. 12, the diskdrive unit 1216 may include a computer-readable medium 1218 in which oneor more sets of instructions 1220, e.g., software, can be embedded.Further, the instructions 1220 may embody one or more of the methods orlogic as described herein. In a particular embodiment or aspect, theinstructions 1220 may reside completely, or at least partially, withinthe main memory 1204, the static memory 1206, and/or within theprocessor 1202 during execution by the computer system 1200. The mainmemory 1204 and the processor 1202 also may include computer-readablemedia.

In an alternative embodiment or aspect, dedicated hardwareimplementations, such as application specific integrated circuits,programmable logic arrays and other hardware devices, can be constructedto implement one or more of the methods described herein. Applicationsthat may include the apparatus and systems of various embodiments oraspects can broadly include a variety of electronic and computersystems. One or more embodiments or aspects described herein mayimplement functions using two or more specific interconnected hardwaremodules or devices with related control and data signals that can becommunicated between and through the modules, or as portions of anapplication-specific integrated circuit. Accordingly, the present systemencompasses software, firmware, and hardware implementations.

In accordance with various embodiments or aspects, the methods describedherein may be implemented by software programs tangibly embodied in aprocessor-readable medium and may be executed by a processor. Further,in an exemplary, non-limited embodiment or aspect, implementations caninclude distributed processing, component/object distributed processing,and parallel processing. Alternatively, virtual computer systemprocessing can be constructed to implement one or more of the methods orfunctionality as described herein.

It is also contemplated that a computer-readable medium includesinstructions 1220 or receives and executes instructions 1220 responsiveto a propagated signal, so that a device connected to a network 1224 cancommunicate voice, video or data over the network 1224. Further, theinstructions 1220 may be transmitted or received over the network 1224via the network interface device 1208.

While the computer-readable medium is shown to be a single medium, theterm “computer-readable medium” includes a single medium or multiplemedia, such as a centralized or distributed database, and/or associatedcaches and servers that store one or more sets of instructions. The term“computer-readable medium” shall also include any medium that is capableof storing, encoding or carrying a set of instructions for execution bya processor or that cause a computer system to perform any one or moreof the methods or operations disclosed herein.

In a particular non-limiting, example embodiment or aspect, thecomputer-readable medium can include a solid-state memory, such as amemory card or other package, which houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals, such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is equivalent to a tangible storage medium. Accordingly, anyone or more of a computer-readable medium or a distribution medium andother equivalents and successor media, in which data or instructions maybe stored, are included herein.

In accordance with various embodiments or aspects, the methods describedherein may be implemented as one or more software programs running on acomputer processor. Dedicated hardware implementations including, butnot limited to, application specific integrated circuits, programmablelogic arrays, and other hardware devices can likewise be constructed toimplement the methods described herein. Furthermore, alternativesoftware implementations including, but not limited to, distributedprocessing or component/object distributed processing, parallelprocessing, or virtual machine processing can also be constructed toimplement the methods described herein.

It should also be noted that software that implements the disclosedmethods may optionally be stored on a tangible storage medium, such as:a magnetic medium, such as a disk or tape; a magneto-optical or opticalmedium, such as a disk; or a solid state medium, such as a memory cardor other package that houses one or more read-only (non-volatile)memories, random access memories, or other re-writable (volatile)memories. The software may also utilize a signal containing computerinstructions. A digital file attachment to e-mail or otherself-contained information archive or set of archives is considered adistribution medium equivalent to a tangible storage medium.Accordingly, a tangible storage medium or distribution medium as listedherein, and other equivalents and successor media, in which the softwareimplementations herein may be stored, are included herein.

Thus, system and method to reconstruct cardiac activation informationhave been described. Although specific example embodiments or aspectshave been described, it will be evident that various modifications andchanges may be made to these embodiments or aspects without departingfrom the broader scope of the invention. Accordingly, the specificationand drawings are to be regarded in an illustrative rather than arestrictive sense. The accompanying drawings that form a part hereof,show by way of illustration, and not of limitation, specific embodimentsor aspects in which the subject matter may be practiced. The embodimentsor aspects illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments or aspects may be utilized and derived therefrom, suchthat structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments or aspects is defined only by the appendedclaims, along with the full range of equivalents to which such claimsare entitled.

Such embodiments or aspects of the inventive subject matter may bereferred to herein, individually and/or collectively, by the term“invention” merely for convenience and without intending to voluntarilylimit the scope of this application to any single invention or inventiveconcept if more than one is in fact disclosed. Thus, although specificembodiments or aspects have been illustrated and described herein, itshould be appreciated that any arrangement calculated to achieve thesame purpose may be substituted for the specific embodiments or aspectsshown. This disclosure is intended to cover any and all adaptations orvariations of various embodiments or aspects. Combinations of the aboveembodiments or aspects, and other embodiments or aspects notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

The Abstract is provided to comply with 37 C.F.R. §1.72(b) and willallow the reader to quickly ascertain the nature and gist of thetechnical disclosure. It is submitted with the understanding that itwill not be used to interpret or limit the scope or meaning of theclaims.

In the foregoing description of the embodiments or aspects, variousfeatures are grouped together in a single embodiment for the purpose ofstreamlining the disclosure. This method of disclosure is not to beinterpreted as reflecting that the claimed embodiments or aspects havemore features than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment or aspect. Thus the followingclaims are hereby incorporated into the Detailed Description, with eachclaim standing on its own as a separate example embodiment or aspect. Itis contemplated that various embodiments or aspects described herein canbe combined or grouped in different combinations that are not expresslynoted in the Detailed Description. Moreover, it is further contemplatedthat claims covering such different combinations can similarly stand ontheir own as separate example embodiments or aspects, which can beincorporated into the Detailed Description.

1. A method of representing cardiac information associated with a heartrhythm disorder, the method comprising: accessing a plurality ofneighboring cardiac signals obtained from a patient; eliminatingfar-field activations from the plurality of neighboring cardiac signalsusing one or more divergence criteria that define local activations inthe plurality of neighboring cardiac signals, the divergence criteriabeing associated with divergence among the plurality of neighboringcardiac signals; and constructing a clinical representation of the localactivations in the plurality of neighboring cardiac signals.
 2. Themethod of claim 1, wherein eliminating far-field activation from theplurality of neighboring signals comprises: accessing a first cardiacsignal and a second cardiac signal of the plurality of neighboringcardiac signals; processing the first cardiac signal and the secondcardiac signal to determine whether there is a point of change in thefirst cardiac signal at which a derivative of the first cardiac signaldiverges with respect to a derivative of the second cardiac signal abovea threshold; and assigning an activation onset time in the first cardiacsignal at the point of change to define a local activation if the pointof change is in the first cardiac signal.
 3. The method of claim 2,wherein the derivative of the first cardiac signal and the derivative ofthe second cardiac signal are selected from the group consisting of azero order derivative, a first order derivative, a second orderderivative, a higher order derivative, and combinations thereof.
 4. Themethod of claim 2, wherein the derivative of the first cardiac signaland the derivative of the second cardiac signal are zero orderderivatives.
 5. The method of claim 2, wherein the derivative of thefirst cardiac signal and the derivative of the second cardiac signal arefirst order derivatives.
 6. The method of claim 2, wherein thederivative of the first cardiac signal and the derivative of the secondcardiac signal are second order derivatives.
 7. The method of claim 2,further comprising obtaining the first cardiac signal and the secondcardiac signal from the patient using a first sensor and a secondsensor, respectively.
 8. The method of claim 7, wherein the firstcardiac signal and the second cardiac signal are obtainedcontemporaneously from the patient.
 9. The method of claim 2, whereinthe point of change is determined at about the same time point for thefirst cardiac signal and the second cardiac signal.
 10. The method ofclaim 2, wherein the point of change is determined from one or more ofslope, amplitude, timing and shape for the first cardiac signal and thesecond cardiac signal.
 11. The method of claim 2, wherein determinationof the point of change comprises: forming a composite cardiac signalfrom the first cardiac signal and the second cardiac signal; determiningratio values at a plurality of points in the first cardiac signal, eachratio value representing a difference between the derivative of thesecond cardiac signal and a derivative of the composite cardiac signalto a difference between derivative of the first cardiac signal and thederivative of the composite cardiac signal; and selecting as the pointof change in the first cardiac signal a point having a largest ratiovalue from the determined ratio values.
 12. The method of claim 2,wherein the threshold is higher than a noise level associated with thefirst cardiac signal and the second cardiac signal.
 13. The method ofclaim 12, wherein a point of change at or below the noise level isassociated with one or more signals from a heart, respiratory system,gastrointestinal tract, neurological system and electronic interference.14. The method of claim 2, further comprising: matching at least onecharacteristic of the first cardiac signal to at least onecharacteristic of a reference cardiac signal in a catalog of cardiacsignals if the point of change is not in the first cardiac signal; andassigning an activation onset time in the first cardiac signal as anactivation onset time of the reference cardiac signal to define a localactivation in the first cardiac signal.
 15. The method of claim 2,further comprising performing accessing, processing and assigning todefine multiple local activations in the first cardiac signal.
 16. Themethod of claim 2, further comprising iteratively accessing the firstcardiac signal and the second cardiac signal from the plurality ofneighboring cardiac signals.
 17. The method of claim 2, furthercomprising: accessing pairs of cardiac signals from the plurality ofneighboring cardiac signals, each pair having a first cardiac signal anddifferent second cardiac signal; performing processing and assigning foreach of the pairs to define multiple local activations in the firstcardiac signal in each of the pairs; and constructing the clinicalrepresentation based on the multiple local activations of the pluralityof neighboring cardiac signals to indicate a source of a cardiac rhythmdisorder.
 18. The method of claim 17, further comprising presenting theclinical representation as constructed to facilitate treatment ofcardiac tissue at the source to treat the cardiac rhythm disorder. 19.The method of claim 1, wherein the divergence criteria comprise signaldivergence, signal amplitude or voltage, signal rate or cycle length,signal upstroke velocity (dV/dt), signal frequency components, andsignal repeatability.
 20. The method of claim 1, wherein the clinicalrepresentation of the local activations include a rotor.
 21. The methodof claim 1, wherein the clinical representation of local activationsincludes a focal source.
 22. A method of treating a heart rhythmdisorder using the clinical representation of claim 1.